IEEE - Aerospace and Electronic Systems - June 2022 - 3

In This Issue -Technically
USING MLSTM AND MULTIOUTPUT CONVOLUTIONAL LSTM ALGORITHMS FOR DETECTING
ANOMALOUS PATTERNS IN STREAMED DATA OF UNMANNED AERIAL VEHICLES
In this article, we present a comparative study oftwo existing deep learning tools that are used in a novel way to detect
anomalies in the streamed data of the unmanned aerial vehicle (UAV). Detecting anomalies is very vital to predict
potential faults that are caused by hardware and software faults and may prevent the UAV from hazardous accidents.
Therefore, we suggest using multiple long short-term memory and multioutput convolutional long short-termmemory
(LSTM) to detect anomalies inUAVdata. LSTMnetworks attractedmany researchers in several domains, as it is a useful
tool for learning dynamic temporal patterns and long-range dependencies in sequential data, which cannot be
achieved using traditional neural networks. However, utilizing multiple LSTM networks would result in too much
redundancy. The redundancy issue can be solved by incorporating one convolutional LSTM (ConvLSTM) network
with multiple outputs. The ConvLSTM is suitable for analyzing multivariate temporal data; due to its convolutional
architecture and the advantage ofpreserving the benefits ofthe LSTM networks. We evaluated and compared the two
approaches using well-known indicators such as the detection rate, the false alarm rate, the precision, and the F.score
indicators. The two methods exhibited promising results in predicting different types of faults, for instance (sensorimpulse
and sensor-cut). However, the multioutput ConvLSTMwas faster in training and testing phases, and its results
were superior in predicting (sensor-stuck and sensor-drift) faults.
GPS SPOOFING DETECTION BY NEURAL NETWORK MACHINE LEARNING
GPS today is ubiquitous. It provides real-time positioning, navigation, and timing (PNT) data for countless military and
civilian users worldwide. Yet, a proliferation ofGPS degrading and denying devices threatens GPS PNT capabilities.
The biggest threat is spoofing, which deceives a GPS receiver into accepting false signals as genuine. Current antispoofing
techniques are highly dependent on specific spoofing scenarios, choice ofmathematical models, values ofthresholds,
and often require significant hardware and/or software modifications to existing GPS receivers. Efforts are also
underway to replace GPS with alternate sources. However, no such sensor can match the availability, accuracy, and
global utility ofGPS. This article examines an alternate antispoofing method using neural networks. The supervised
machine learning technique uses well-understood GPS observables such as pseudorange, carrier phase, Doppler shift,
and carrier-to-noise density ratio to distinguish between authentic and spoofed signals. Network architecture, training
sample size, number of hidden layers, distribution of neurons between hidden layers, and number of hidden neurons
are examined. Results show that the proposed method is capable ofdetecting spoofing signals with a high probability
ofcorrect classification and a low probability ofmisclassification.
COGNITIVE RADIO FOR AERONAUTICAL MOBILE TELEMETRY: A MACHINE LEARNING-BASED
APPROACH
The need for a cognitive radio in aeronautical mobile telemetry is motivated by the simultaneous use of three different
versions of continuous phase modulation and bitrates that vary from test article to test article. The cognitive
radio envisioned in this article comprises two parts: a machine learning algorithm that determines modulation
type and bitrate, and a software-defined radio that performs demodulation and detection. The experiments presented
in this article show that a quadratic discriminant classifier operating on power spectral density (PSD) estimates
can successfully identify the modulation. Following the classifier, a Gaussian process regression model
operating on the same PSD estimate can estimate the bitrate.
REINFORCEMENT-LEARNING-BASED TASK PLANNING FOR SELF-RECONFIGURATION OF
CELLULAR SATELLITES
Cellular satellites, which are composed ofmany standard unit cells, represent a class of novel and promising satellites
for future space explorations. Their potentials have been well recognized in the aerospace field. Themost attractive
feature ofcellular satellites is the on-orbit self-reconfiguration capability through cell-by-cell moves. However,
it is extremely challenging for a cellular satellite to autonomously achieve the optimal self-reconfiguration with fewest
cell moves, because the search space for legal actions may be larger than that ofthe game ofGo ifthe satellite has
a certain number of cells. In this article, we propose a reinforcement learning-based task planning strategy for the
self-reconfiguration of cellular satellites. Inspired by the recent progress ofAlphaGo and AlphaGo Zero, we calculate
the cell move sequence and predict the cell placements in the self-reconfiguration process by combining the
Monte Carlo tree search and the neural network. The reinforcement learning-based task planning strategy is validated
by comparing with the traditional melt-sort-grow algorithm. The validation results demonstrate that the proposed
strategy can significantly reduce the number ofcell moves for the self-reconfiguration ofcellular satellites.
JUNE 2022
IEEE A&E SYSTEMS MAGAZINE
3

IEEE - Aerospace and Electronic Systems - June 2022

Table of Contents for the Digital Edition of IEEE - Aerospace and Electronic Systems - June 2022